Structure-based pose prediction: Non-cognate docking extended to macrocyclic ligands.

J Comput Aided Mol Des

BioPharmics Division, Optibrium Limited, Cambridge, CB25 9PB, UK.

Published: October 2024

AI Article Synopsis

  • Cross-docking involves predicting how new small-molecule ligands will bind to a protein when these ligands are different from the known binding partners, which is a more complex task than re-docking the original ligands.
  • The updated PINC benchmark uses temporal segregation to evaluate cross-docking accuracy, including predictions for 846 future ligands across ten protein targets and an extension for 128 macrocyclic ligands based on data from previously known non-macrocyclic ligands.
  • Overall, using methods like Surflex-Dock and ForceGen, the study achieved impressive success rates, with 68% accuracy for the best poses and 92% for any correct poses, outperforming alternative methods like AutoDock Vina and

Article Abstract

So-called "cross-docking" is the prediction of the bound configuration of small-molecule ligands that differ from the cognate ligand of a protein co-crystal structure. This is a much more challenging problem than re-docking the cognate ligand, particularly when the new ligand is structurally dissimilar from prior known ones. We have updated the previously introduced PINC ("PINC Is Not Cognate") benchmark which introduced the idea of temporal segregation to measure cross-docking performance. The temporal set encompasses 846 future ligands for ten targets based on information from the earliest 25% of X-ray co-crystal structures known for each target. Here, we extend the benchmark to include thirteen targets where the bound poses of 128 macrocyclic ligands are to be predicted based on knowledge from structures of bound non-macrocyclic ligands. Performance was roughly equivalent for both the temporally-split non-macrocyclic ligand set and the macrocycle prediction set. Using standard and fully automatic protocols for the Surflex-Dock and ForceGen methods, across the combined 974 non-macrocyclic and macrocyclic ligands, the top-scoring pose family was correct 68% of the time, with the top-two pose families achieving a 79% success rate. Correct poses among all those predicted were identified 92% of the time. These success rates far exceeded those observed for the alternative methods AutoDock Vina and Gnina on both sets.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11485047PMC
http://dx.doi.org/10.1007/s10822-024-00574-0DOI Listing

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